{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-04T03:09:09.626061Z",
     "start_time": "2025-01-04T03:09:07.787893Z"
    }
   },
   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt",
   "id": "ded3f5672d8acee1",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-04T06:17:34.261660Z",
     "start_time": "2025-01-04T06:17:34.165047Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#第一个是x坐标，第二个是y坐标\n",
    "plt.plot([2,3,9],[4,5,6])\n",
    "#展示图像\n",
    "plt.show()"
   ],
   "id": "360fa9ba13d9c22f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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